sis 18: e37

Research Article

Multi-attention mechanism based on gate recurrent unit for English text classification

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  • @ARTICLE{10.4108/eai.27-1-2022.173166,
        author={Haiying Liu},
        title={Multi-attention mechanism based on gate recurrent unit for English text classification},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2022},
        month={1},
        keywords={English text classification, multi-attention mechanism, GRU, deep learning},
        doi={10.4108/eai.27-1-2022.173166}
    }
    
  • Haiying Liu
    Year: 2022
    Multi-attention mechanism based on gate recurrent unit for English text classification
    SIS
    EAI
    DOI: 10.4108/eai.27-1-2022.173166
Haiying Liu1,*
  • 1: School of Foreign Languages, Zhengzhou University of Science and Technology, Zhengzhou 450064 China
*Contact email: snowycry@qq.com

Abstract

This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173791. Text classification is one of the core tasks in the field of natural language processing. Aiming at the advantages and disadvantages of current deep learning-based English text classification methods in long text classification, this paper proposes an English text classification model, which introduces multi-attention mechanism based on gate recurrent unit (GRU) to focus on important parts of English text. Firstly, sentences and documents are encoded according to the hierarchical structure of English documents. Second, it uses the attention mechanism separately at each level. On the basis of the global object vector, the maximum pooling is used to extract the specific object vector of sentence, so that the encoded document vector has more obvious category features and can pay more attention to the most distinctive semantic features of each English text. Finally, documents are classified according to the constructed English document representation. Experimental results on public data sets show that this model has better classification performance for long English texts with hierarchical structure.